The article [1]} shows a new training principle in order to extract robust features with the denoising AEs. The main idea of the presented approach is that a noise-damaged sample is processed by the AE as an input and the original sample is considered as a label. For the input corruption, the authors set a fixed number of randomly selected input elements to zero, but they suggest a use of other corrupting noise. This approach should help the AE to select stronger features and to better generalize the inputs.
